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Title: Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

Journal Article · · Energies
DOI:https://doi.org/10.3390/en10010040· OSTI ID:1347528
ORCiD logo [1];  [1]
  1. Univ. of Miami, Coral Gables, FL (United States)
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Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.

Research Organization:
Univ. of Miami, Coral Gables, FL (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Grant/Contract Number:
EE0005545
OSTI ID:
1347528
Journal Information:
Energies, Vol. 10, Issue 1; ISSN 1996-1073
Publisher:
MDPI AGCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 54 works
Citation information provided by
Web of Science

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Cited By (12)

Risk Evaluation of Distribution Networks Considering Residential Load Forecasting with Stochastic Modeling of Electric Vehicles journal May 2019
An agent-based approach to power system dynamic state estimation through dual unscented Kalman filter and artificial neural network journal February 2019
Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine journal October 2019
Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation journal August 2017
A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation journal March 2018
Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting journal April 2018
Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees journal August 2018
A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction journal October 2019
Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data journal May 2018
Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities journal February 2019
Forecasting Irregular Seasonal Power Consumption. An Application to a Hot-Dip Galvanizing Process journal December 2020
Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables journal April 2018

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Related Subjects

30 DIRECT ENERGY CONVERSION
short-term load forecasting
nonlinear autoregressive exogenous input
artificial neural networks
closed-loop forecasting